JP7159202B2 - Computer-implemented methods, systems and computer program products forming a blockchain for public scientific research (Blockchain for Public Scientific Research) - Google Patents

Description

The present disclosure relates to integrating blockchain and data collection and analysis for open scientific research.

Currently, there are a limited number of platforms that allow sharing information about scientific research and demonstrating transparent data collection and analysis steps. Platforms that do exist lack the controls and mechanisms required to enable trusted data. The reason is that there are few options to ensure that the data is resistant to modification.

For example, Topol, Money Back Guarantees for Non-Reproducible Results, BMJ 2016, 353:i2770, published 24 May 2016, states, "The problem of irreproducibility in biomedical research is real and has been reported in multiple reports. "The use of blockchain technology has recently been shown to provide an immutable ledger for every step in a clinical research protocol, which is , can be easily adapted to basic and experimental model science.All participants in the peer-to-peer research network have access to all of the time-stamped and continuously updated data. It is tamper-resistant because any changes, such as those to data analysis, must be made on all (typically thousands of) computers inside the distributed network. proof) is essential.” The paper by Topol, which describes the problem of data transparency and proposes that blockchains could serve as a solution, urges researchers to consider public blockchains. ), as having access to time-stamped, immutable data. This paper states that via 1) a mixed confidentiality policy, 2) researchers with access to real-time logs of the analysis (only changes to the analysis plan), 3) connection of the analysis software to the blockchain contract. 4) any method or algorithm (e.g., automatic correction for multiple parses) to assess the statistical power of the underlying results from parsing the steps in the blockchain; or 5) for any integrated algorithm or blockchain contract that performs functions other than a) a) public record of data transactions and b) refunds based on detection of data provenance violations, not explained.

Similarly, Irving et al., How Blockchain-Timestamped Protocols could Improve the Trustworthiness of Medical Science, F1000Research 2016, 5:222, last updated 31 May 2016, states that “Blockchain proof-of-concept research could report as a low-cost, independently verifiable method that can be widely and easily used to audit and confirm reliability. Similar to the article by Topol, the article by Irving et al. does not disclose points 1 to 5 above.

U.S. Pat. No. 7,404,079 to Gudbjartsson et al. states that "personally identifiable data transmitted by one party, once received by a second party, may be considered anonymous." , discloses an automated system for processing data packets composed of identifiers and data," and that "the invention facilitates distributed key management of mapping functions and strong authentication. using secret-sharing technology that allows the system to be operated remotely by The above US patent to Gudbjartsson discloses a hybrid security policy that may be an example of what may be used as an antecedent agreement to the inventions described herein. However, it is not only such security agreements that may be used, which differ from the subject matter of this disclosure in the following ways: 1) a prior agreement between all parties; (up-front) security agreements and not security policies defined by the parties at the time they expose their data to the blockchain; 2) do not specify a public blockchain ledger; 3) data and 4) do not specify smart contracts that treat reported or parsed data differently than raw data. , is different.

U.S. Pat. No. 7,404,079

Topol, Money Back Guarantees for Non-Reproducible Results, BMJ 2016, 353:i2770, published 24 May 2016 Irving et al., How Blockchain-Timestamped Protocols could Improve the Trustworthiness of Medical Science, F1000Research 2016, 5:222, last updated 31 May 2016

Share information about scientific research and demonstrate transparent data collection and analysis steps.

SUMMARY The following presents a summary to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or critical elements or to delineate any scope of any particular embodiment or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, a system, computer-implemented method, apparatus, or computer program product, or combination thereof, that facilitates synchronization of processors for blockchain formation is described. be done. The shortcomings of the cited references noted in the background section above have been overcome using the features disclosed herein.

According to one embodiment, a computer-implemented method is provided. The computer-implemented method includes generating, by a device operatively coupled to the processor, a first block from a first file, the first block including a first header and experimental data in a first file. contained within one file, the first header further comprising a first timestamp, an identifier identifying the source of the experimental data, and a first hash based on the experimental data; and generating a second block based on a second file containing a log of the analysis performed on the experimental data by the device, the second block comprising the log of the analysis and the second and a second header including a timestamp of and a link to the first block and a second hash based on the log of the analysis. According to this embodiment, a blockchain is formed based on the collected data, and analysis is performed to provide a tamper-resistant log of scientific research, thus achieving advantages over the prior art.

In an optional embodiment, the computer-implemented method comprises performing, by the device, corrections to the analysis results based on a review of the summary of the experimental data of the third block; Further comprising generating a fourth block and adding the fourth block to the blockchain by the device. According to this embodiment, an advantage over the prior art is realized because the corrected analysis result results in higher reliability and accuracy.

In an optional embodiment, the computer-implemented method further comprises encrypting the experimental data and the analysis log prior to forming the first block and the second block, respectively, by the device. According to this embodiment, prior to forming the first block and the second block, the experimental data and the log of the analysis are encrypted to improve security and to prevent tampering using such information. Advantages over the prior art are realized because the opportunities for ringing and/or otherwise modifying such information are reduced.

In another embodiment, a system may include a memory storing computer-executable components and a processor executing the computer-executable components stored in the memory. The computer-executable components may include a data collection component that creates a master data block from a data entry blockchain, which is a group of data entry blocks that are linked together. blocks, the master data block including the first header and data from the data entry block. The header may further include a first timestamp, an identifier identifying the source of the data, and a first hash based on the data. The computer-executable component also includes a second hash that includes a log of analysis performed on the data and a second timestamp and a link to the master data block and a second hash based on the log of the analysis. It may also include a parsing component that creates a parsing block that includes a header, and a parsing component. The analysis block and master data block contain the blockchain. According to this embodiment, a blockchain is formed based on the collected data, and analysis is performed to provide a tamper-resistant log of scientific research, thus achieving advantages over the prior art.

In an optional embodiment, the computer-executable components may include a correction component that assesses the reliability of the results of the analysis based on the analysis summary and the analysis log, where reliability is the result of the analysis. is related to the number of attempts to achieve . This reliability assessment is an advantage over the prior art because it provides an objective basis for determining the amount of confidence to be placed in the results of scientific research or the conclusions reached.

In another embodiment, a computer-implemented method is provided. The computer-implemented method may include forming, by a device operatively coupled to a processor, a blockchain representing a research project. The blockchain includes a first block of research data and a second block of analysis data representing a log of analyzes performed on the research data. The computer-implemented method can also include forming, by the device, a summary block that includes a summary of the study data and a summary of the analysis data. The computer-implemented method may also include adding a summary block to the blockchain by the device. The summary block provided in this embodiment can be added to the blockchain to represent the conclusions reached by researchers and to provide a template for facilitating the publication of scientific papers on research. .

In another embodiment, a system may include a memory storing computer-executable components and a processor executing the computer-executable components stored in the memory. The computer-executable component can include a data collection component that receives a first file having experimental data and generates a first block from the first file. The first block includes a first header and experimental data, the header further including a first timestamp, an identifier identifying the source of the experimental data, and a first hash based on the experimental data. The computer-implemented component is also an analysis component that generates a second block based on a second file containing a log of the analysis performed on the experimental data, the second block being the log of the analysis. a second header, the second header including a second timestamp, a link to the first block, and a second hash based on a log of the analysis; and experimental data. and a test component that generates a third block containing a summary of the analysis and the results of the analysis. According to this embodiment, a blockchain is formed based on the collected data, and analysis is performed to provide a tamper-resistant log of scientific research, thus achieving advantages over the prior art.

In an optional embodiment, the computer-executable component further includes a correction component that assesses the reliability of the results of the analysis based on the analysis summary and the analysis log, where the reliability is the result of the analysis. is related to the number of attempts to achieve According to this embodiment, since the reliability of the analysis result is evaluated, an effect exceeding the prior art is realized.

According to yet another embodiment, a computer program product is provided for generating a blockchain using publicly available scientific data. The computer program product may include a computer-readable storage medium having program instructions embodied using the computer-readable storage medium. Program instructions may be executable by a processor to cause the processor to generate a master data block from a data entry blockchain. A data entry blockchain includes a group of data entry blocks that are linked together, a master data block including a first header and data from the data entry blocks, the header being , a first timestamp, an identifier identifying a source of the data, and a first hash based on the data. The processor also generates a second header including a log of analysis performed on the data, a second timestamp and a link to the master data block and a second hash based on said log of analysis. , may be generated. The analysis block and master data block contain the blockchain. According to this embodiment, a blockchain is formed based on the collected data, and analysis is performed to provide a tamper-resistant log of scientific research, thus achieving advantages over the prior art.

In an optional embodiment, the program instructions are further executable to cause the processor to evaluate the reliability of the results of the analysis based on the analysis summary and the analysis log, wherein the reliability determines the results of the analysis. It is related to the number of trials you are trying to achieve. According to this embodiment, the reliability of the analysis result is judged by using the evaluation of the analysis summary and the analysis log, so that an advantage over the prior art is realized.

Embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which: FIG.

1 is a high-level block diagram of an exemplary non-limiting blockchain system, according to one or more embodiments described herein; FIG. FIG. 2B is a high-level block diagram of another exemplary, non-limiting blockchain system, according to one or more embodiments described herein; 4 is a flowchart of an exemplary, non-limiting method of integrating blockchain functionality with published scientific research, according to one or more embodiments described herein. 1 is a block diagram of an exemplary, non-limiting system for forming data blockchains from separate data sets, according to one or more embodiments described herein; FIG. FIG. 4B is another block diagram of an exemplary, non-limiting system for forming an analysis blockchain, according to one or more embodiments described herein; FIG. 4 is another block diagram of an exemplary, non-limiting, public research blockchain, according to one or more embodiments described herein; FIG. 4 is another block diagram of an exemplary, non-limiting system of headers and data portions of a data blockchain, according to one or more embodiments described herein; 4 is a flowchart of an exemplary, non-limiting, computer-implemented method for forming a blockchain based on research data and analytics, according to one or more embodiments described herein. 4 is another flow chart of an exemplary, non-limiting, computer-implemented method of forming a blockchain based on research data and analytics, according to one or more embodiments described herein. 1 is a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein may be facilitated; FIG. 1 is a block diagram of an exemplary, non-limiting cloud computing environment, in accordance with one or more embodiments of the present invention; FIG. 1 is a block diagram of an exemplary non-limiting abstract model layer, in accordance with one or more embodiments of the present invention; FIG.

The following detailed description is illustrative only and is not intended to limit the embodiments or the application and/or uses of the embodiments. Furthermore, there is no intention to be bound by any information presented, expressed or implied in the preceding Background or Summary section or the Detailed Description section. do not do.

The effects and contributions of this disclosure are: 1) a mixed confidentiality policy, 2) researchers with access to real-time logs of analyzes (not just changes to analysis plans), 3) blockchain contracts for analysis software. 4) any method or algorithm for evaluating the statistical power of the underlying results from analyzing the steps in the blockchain (e.g., multiple analysis or 5) any integrated algorithm that performs functions other than refunds based on a) public records of data transactions and b) detection of data provenance violations. or blockchain contracts, or any combination thereof.

One or more embodiments are now described with reference to the drawings, where like reference numbers are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are given in order to provide a more thorough understanding of one or more embodiments. It is evident, however, that in various instances one or more embodiments may be practiced without these specific details.

Although this disclosure includes detailed discussion regarding cloud computing, it should be understood that implementation of the teachings described herein is not limited to cloud computing environments. Rather, embodiments of the invention may be implemented with any other type of computing environment now known or later developed.

Cloud computing refers to configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage) that can be rapidly provisioned and released with minimal administrative effort or interaction with service providers. It is a model of service delivery that enables convenient, on-demand network access to a shared pool of (i.e., applications, virtual machines, and services). This cloud model may include at least five features, at least three service models, and at least four deployment models.

Features are as follows.
On-demand self-service: Cloud consumers automatically supply computing capabilities, such as server time and network storage, as needed without requiring human interaction with the provider of the service. It can be supplied unilaterally.
Broad Network Access: Capabilities are available over the network and accessed through standard mechanisms facilitating use by disparate thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). be.
Resource Pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and distributed according to demand. redistributed. Consumers generally do not have control or knowledge of the exact location of the resources provided, although at a higher level of abstraction (e.g. country, state, data center) it is possible that location can be determined. At points, there is a sense of position independence.
Rapid flexibility: Capacity can be rapidly and flexibly provisioned, in some cases automatically, to scale out immediately, and released quickly to scale in immediately. For consumers, the capacity available for supply is often seemingly unlimited and can be purchased in any amount at any time.
Measured Services: Cloud systems automate resource usage by leveraging the ability to measure at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). control and optimize Resource usage can be monitored, controlled and reported, providing transparency to both providers and consumers of the services utilized.

The service model is as follows.
Software as a Service (SaaS): The ability offered to the consumer is to use the provider's applications running on top of the cloud infrastructure. Applications are accessible from various client devices through a thin client interface such as a web browser (eg, web-based email). Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, storage, or even individual application capabilities, with possible exceptions being limited user-specific application configuration settings.
Platform-as-a-Service (PaaS): The ability offered to the consumer allows the consumer-created or acquired application written using programming languages and tools supported by the provider to be placed on top of cloud infrastructure. It is to expand. Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but the deployed applications and potentially the environment configuration hosting the applications. You have control over rations.
Infrastructure as a Service (IaaS): The capabilities offered to the consumer can include processing, storage, networking, or operating systems and applications, or else the consumer can deploy and run any software to provide basic computing resources for Consumers do not manage or control the underlying cloud infrastructure, but have control over the operating system, storage, deployed applications, and potentially (e.g., host firewalls, etc.) Has limited control over selected networking components.

The deployment model is as follows.
Private cloud: This cloud infrastructure is operated exclusively for an organization. It may be managed by this organization or by a third party and may exist within or outside the organization.
Community Cloud: This cloud infrastructure is shared by several organizations and supports a specific community that shares interests (eg, mission, security requirements, policies, and compliance considerations, etc.). It may be managed by these organizations or by third parties and may exist in-house or out-of-organization.
Public Cloud: This cloud infrastructure is made available to the general public or large industrial groups and is owned by an organization that sells cloud services.
Hybrid cloud: This cloud infrastructure remains a unique entity, but with standardized or proprietary technologies that enable data and application portability (e.g., for load balancing across multiple clouds). A mixture of two or more clouds (private, community, or public) interconnected by a cloud bursting of

Cloud computing environments are service-oriented, with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

A problem currently exists due to the limited platforms that allow sharing information about scientific research and demonstrating transparent data collection and analysis steps. As a result, various persons contributing to research and/or publication may not receive accurate and/or sufficient recognition of the work performed. Furthermore, currently existing platforms lack the requisite controls and mechanisms that enable trustworthy data, with few options for ensuring that data is resistant to modification. .

In various embodiments disclosed herein, multiple solutions address the above problems. For example, in one or more embodiments, the trustworthiness of a blockchain concept is determined by generating a blockchain of experiments formed, data collected, analysis performed, or results achieved, or combinations thereof. A blockchain system will be provided that integrates with public scientific research. Integrate scientific and blockchain processes to improve the reliability and/or reproducibility of data and/or results due to the inherent nature of blockchain to be resistant to modification It becomes possible to let Blockchain can also be used to analyze the reliability and provenance of data. A blockchain system can form a blockchain representing a research project, where the blockchain represents the first block of research data and the log of the analysis performed on the research data. and a second block of analytical data. Summary and correction blocks may also be added to the blockchain, representing post hoc analysis of research results. Using information in block headers that can serve to determine whether modifications to a block have been made, subsequent blocks can be compared to previous blocks while also preserving the benefit of confidentiality in research data. may be linked.

In certain embodiments, the disclosure also provides for scientists and other researchers to perform experiments and/or otherwise collect research data, perform analyzes on the data, and reach conclusions. methods for making corrections, making corrections, or tracking and recording their work, or any combination thereof, should also be used by other investigators and scientists to determine whether the data or results were obtained by the original investigator or during the process. conduct peer reviews, attempt to replicate the results, or consider the relevance and importance of the study as a whole, without worrying about whether it has been manipulated in any other step; To perform the combination can be provided. The blockchain system, in some embodiments, could be integrated with the cloud system and track data being uploaded to public databases. In some embodiments, the blockchain system is also integrated into researcher consoles and other applications that perform data collection and analysis to obtain real-time updates of the analysis being performed on the data. It is also possible. In some embodiments, blockchain systems may also facilitate working with private research data. Instead of using data from public databases, data could be retrieved from private databases and encrypted. A blockchain system may form a blockchain from data and analytical information that is encrypted.

Referring now to FIG. 1, illustrated is a high-level block diagram 100 of an exemplary non-limiting blockchain system 102 according to one or more embodiments described herein. . In FIG. 1, blockchain system 102 may include processor 104 , data collection component 106 , analysis component 108 , inspection component 110 , and correction component 112 . In various embodiments, one or more of processor 104 , data collection component 106 , analysis component 108 , verification component 110 , and correction component 112 are one or more components of blockchain system 102 . may be electrically and/or communicatively coupled to each other to perform functions. The blockchain system 102 may receive 114 data from scientific experiments and produce 116 processed information ready for publication or publication.

In some embodiments, blockchain system 102 may be a cloud-based system that enables formation of blockchains for various steps in an experimental/scientific research process. In other embodiments, blockchain system 102 may be based on a network or device communicatively coupled to a system performing data collection and analysis or executing a program. . In some embodiments, blockchain system 102 may include processor 104 executing computer-executable components stored in memory. These components may include data collection component 106, which may create a master data block (eg, data block 206) from the data entry blockchain. The data entry blockchain can be based on data received 114 from data collection steps in scientific experiments or from public ledgers (see, eg, FIG. 2). A data entry blockchain may include a group of data entry blocks (eg, data blocks 402 and/or 404) that are linked together. Blocks are linked when the hash of the preceding block is included in the header of the following block. Since each block can have a unique hash, the linked hash in the header is a backward reference to a particular block, ie a blockchain of multiple blocks. The master data block may contain data from the first header and/or data entry block. The header may further include a first timestamp representing when the data was collected and/or when the master data block was formed and/or uploaded to the public ledger. . The header may also include an identifier identifying the source of the data and a first hash based on the data. The identifier can be a serial number associated with a research group or scientist, or can be associated with a device that collects data (eg, a measuring instrument, etc.).

Analysis component 108 may include data representing a log of analysis performed on the data and/or a second header containing a second timestamp (e.g., when the analysis block was formed). For example, analysis block 502 or 504), a URL link to the master data block, or a second hash based on the log of analysis or a combination thereof may be generated. The analysis block and/or master data block includes a blockchain (eg, linked blocks). Analysis logs can be based on the researcher's console output tracking computations and other modeling performed by the researchers. Logs may include both the operations and algorithms performed on the data, as well as the results of the processing.

The inspection component 110 may include a summary block or inspection component (e.g., inspection component 602) containing the data summary, the analysis summary and the results of the analysis, and/or a third header containing a link to the analysis block. , can be created. The components may also include a correction component 112 that assesses the reliability of the results of the analysis based on the analysis summary and/or analysis log, which reliability depends on attempts to achieve the analysis results. related to the number of times. For example, an analysis sequence that has many steps and/or attempts to fit the data to the model may have a lower confidence rating than an analysis sequence that has fewer steps.

The blockchain system 102 and/or the components of the blockchain system 102 are not abstract and highly technical problems that cannot be performed as a set of human mental actions (e.g., (bioinformatics, authentication, compression, big data analysis, etc.) can be used in hardware and/or software. Blockchain system 102 and/or system components arise through advances in technology (e.g., data provenance, research reliability/integrity, etc.), computer networks, the Internet, or the like or combinations thereof It can be used to solve new problems.

Processor 104 may be associated with at least one of a central processor, a graphical processor, and the like. In various embodiments, processor 104 may be hardware, software (eg, a set of threads, a set of processes, running software, etc.), or for machine learning computing tasks (eg, received machine learning computing tasks associated with data obtained from data processing), or a combination of both, hardware and software. For example, processor 104 can run data analysis threads that cannot be run by a human (eg, beyond the capabilities of a single human mind). For example, the amount, data processing rate and/or data type processed by processor 104 during a period of time is the amount, rate, or rate that can be processed by a single person during the same period of time. and data types, respectively, can be more, faster, and more extensive. For example, the data processed by processor 104 may be raw data captured by one or more sensors and/or one or more computing devices (e.g., raw audio data, raw video data, etc.). data, raw text data, raw numeric data, etc.) or compressed data (e.g., compressed audio data, compressed video data, compressed text data, compressed numeric data, etc.) Or it can be both. In addition, processor 104 may also be fully responsible for performing one or more other functions (eg, fully powered on, fully executed, etc.) while also processing data analysis data and run-time environment data as described above. can be operational.

Referring now to FIG. 2, another high-level block diagram 200 of an exemplary non-limiting blockchain system 102 is illustrated in accordance with one or more embodiments described herein. ing. Repetitive descriptions of similar elements used in other embodiments described herein are omitted for purposes of brevity.

In some embodiments, data collection component 106 may receive research and/or experimental data from database 202 . Database 202 may be a public ledger or a private database associated with one or more of the research groups. The database may store data in block form as represented by blocks 204, 206, or 208, or a combination thereof, having header portions and/or data portions. In some embodiments, database 202 may store data in raw form separated by experiment performed, researcher collecting data, subject, participant, or other distinguishing factor.

Referring now to FIG. 3 , illustrated is a flowchart of an example non-limiting computer-implemented method 300 for integrating blockchain functionality with open scientific research in accordance with one or more embodiments described herein. It is Repetitive descriptions of similar elements used in other embodiments described herein have been omitted for purposes of brevity.

In some embodiments, the flowchart may begin at 302 where an experiment or project identifier (ID) may be obtained and/or otherwise established. A project ID may be used in the headers of the blocks that form the blockchain as a way of identifying the blockchain and associating it with an experiment or research project.

A project ID may be attached to the data collected at 304 . The data collected at 304 can be associated with data from a research project or experiment, data collected from a previously conducted project or experiment, or as a project or experiment is in progress. It may include data collected at the same time. This data may be data collected from one or more scientific instruments, data collected from people providing feedback (e.g. interviews, observational data, etc.), or online assessments, data harvesting, big data It may include other data obtained via data collections and the like.

In some embodiments, the data collected at 304 can be segregated into data sets from individuals, from groups of researchers, or from different experiments or the same experiment performed at different times. In some embodiments, the blockchain system may generate one block containing the data, and in other embodiments, the blockchain system may generate separate blocks, although the In some cases, separate data sets are formed in one block. A block can contain a data portion and/or a header, which contains a project ID and other information that can be used to link blocks together. In some embodiments, blocks may be formed within a blockchain to maintain data and make it resistant to modification. Blocks are formed in the blockchain by including the hash of a previous block in the header of a subsequent block. Chains of hashes from preceding blocks to subsequent blocks result in blockchains, but they are resistant to modification due to the nature of hashes. Since the hash is a unique number based on the data inside each block, any change to the data results in a new hash. A new hash will create a discontinuity in the blockchain, making it disappear.

In some embodiments, data may be collected from public or non-public databases, ie, blockchains may be stored in public or non-public databases. A link that connects multiple blocks in a blockchain can be a Uniform Resource Locator (URL) or other database link. In some embodiments, this link may be a hash of the previous block. Once a first block is hashed, this hash can uniquely identify that block, and subsequent blocks can be hashed by including the hash of the first block in the header of the second block. It can be linked to the first block.

In one embodiment, in response to data being collected at 304 (eg, by data collection component 106), data may be written into a block, or in another embodiment, a block may Formed based on data. When another or subsequent block is created, it can then be linked to the first/previous block. Note that a timestamp may be included in the header to identify the order in which the blocks were received. If multiple blocks are collected at the same time, and thus have the same timestamp, then the order in which they are written or collected to the online database and/or server (e.g., database 202) is determined by the blockchain. Establish the order in which those blocks are placed.

At 306, analysis component 108 may perform processing. By way of example and not limitation, processing may include adding blocks to the blockchain that represent initial data reductions that are not associated with formal analysis to fit the data to one or more models. Data reduction and other processing steps may be included in process 306 . These steps may include hash generation by processor 104 executing a hash function that generates a unique number based on the data in the block. In other embodiments, the processing steps may include data reduction, manipulation, and other processing steps performed on the data to organize the raw data. Processing steps can also be computations and other algorithms performed on data to try to make the data conform to one or more hypothesized models. This processing may also include other data modeling functions performed on the data collected at 304 .

In some embodiments, processing steps may be determined based on one or more analysis logs collected from research groups, public or private databases, or console applications that performed the analysis. In some embodiments, the blockchain system may generate a parsing block that includes these steps and a header with information used to link the parsing block to the blockchain formed at 304 .

At 308, analysis component 108 may generate a series of analysis blocks. In this case, one or more analysis blocks are associated with one or more of the calculations and other processing functions. In this way, processing steps can be saved on the blockchain for review and correction at a later point in time. In some embodiments, one or more of the analysis blocks may correspond to separate data blocks or one or more analyzes performed on the dataset associated with that data block. can contain.

At 310, the testing component 110 may validate participants both during data collection at 304 as well as during processing/analysis steps at 306 and/or 308. FIG. Identifying participants includes, but is not limited to, to determine the source of the data and which investigator, study group, or other participant went through one or more analysis steps. may include examining the headers of one or more of the data blocks to determine if the data has been accessed and/or modified.

In one or more of these preceding and following steps, the blockchain system is publicly or privately managed so that other entities can access the blockchain to inspect it and view the research process. Recognizing that a blockchain in a public ledger/database can be updated in real-time or at predefined intervals (e.g., every x minutes or when blocks are added to the blockchain) It should be.

At 312, an inspection process is performed, in which the inspection component 110 summarizes the data and analysis steps 306, 308 performed so far to determine the outcome of a research project or other conclusion. can. In some embodiments, the inspection component may include only the headers of the data blocks and analysis blocks for backward linking. In additional embodiments, the inspection component could have a header containing a hash that is the hash of the last parsed block.

The verification step 312 also obtains information and/or data regarding research projects or experiments outside the blockchain and applies it to the blockchain to confirm the accuracy and reliability of those external information. may be compared with the results and data reported in .

In another embodiment, the blockchain system 102 may include one or more protocols whereby the verification component, until verified to incorporate the outputs of the parsing blocks and their hashes, may: Not added to the blockchain or otherwise registered. By including the hash, you not only ensure that the identifier of which block in the blockchain results in that outcome, but also that you can use the hash to verify the authenticity of the block and the outcome. be done.

At 314, correction component 112 may implement corrections to determine the relative reliability or importance of the conclusions of the study and/or experiment. For example, if a relatively small number of analytical steps or modifications of a processing algorithm are required to achieve a postulated result, the conclusion of the experiment or study will be more likely than if many modifications were performed. It may have a high weighted importance. By determining which of the parse blocks resulted in an outcome, the blockchain system can determine how many modifications are performed, with earlier parse blocks yielding results. If an analysis block eventually produced an outcome, that outcome may be weighted more highly than if a later analysis block resulted in an outcome. The blockchain system could also determine the weighting based on the type of modification reported in the analysis block.

At 316, the correction component 112 can help publish the research project. To format the information so that it is otherwise readily accessible to one or more of the researchers when the blockchain system collects and publishes the data and authors the papers. , the data block, analysis block, test component (including summary and outcome), and correction block can be accessed.

At 318, the review component 110 may complete and store the blockchain and other information in one or more public ledgers or databases, or private databases for peer review, which may occur at 320.

Referring now to FIG. 4, an exemplary non-limiting set of data blocks forming a data blockchain from separate data sets in accordance with one or more embodiments described herein. A block diagram 400 is illustrated. Repetitive descriptions of similar elements used in other embodiments described herein are omitted for purposes of brevity.

When a blockchain system (eg, blockchain system 102) collects data (as described in step 304 above), separate data sets such as data block A 402 and data block B 404 to form a larger data block 406 that includes data blocks 402 and 404 .

In some embodiments, the collected data used to generate data blocks 402 and 404 may be associated with data from research projects or experiments, and may be from previously conducted projects or experiments. It may include collected data or data collected while a project or experiment is in progress. As an example, data block A 402 may be associated with data received from a first experiment and data block B 404 may be from a second experiment or a second run of the first experiment. It can be associated with the received data. Data were obtained from one or more scientific instruments, from people providing feedback (e.g., interviews, observational data, etc.), or via online assessments, data harvesting, big data collection, etc. It may include data gathered from other data.

In some embodiments, the data collected is, for example, data block A 402 and data block B 404 from multiple individuals, multiple groups of multiple researchers, or different experiments or the same experiment performed at different times. etc., can be separated into multiple datasets. In some embodiments, a blockchain system may generate one block containing data. In another embodiment, the blockchain system may generate separate blocks, where one or more of the separate data sets are formed within one block. A block may contain a data portion and a header, which contains a project ID and other information that may be used to link the blocks together. In some embodiments, one or more of the blocks may be formed in a blockchain to hold data and make it resistant to modification.

In some embodiments, data in one or more of data blocks A 402 and B 404 may be encrypted. In some embodiments, some or all of the data in a block may be encrypted, or in other embodiments the data portion of the block is encrypted to protect the source of the data. The header may be encrypted whereas not.

A data block in the blockchain (eg, data blocks 402 and 404) may include a hash in the header of the preceding data block. A hash may link one or more blocks together and may provide a way to determine if any data modification has been performed. A blockchain system can compare hashes and, if the hashes do not match each other, can indicate that a modification or change has been made to a data block with a mismatching hash.

Referring now to FIG. 5, another block diagram 500 of an exemplary blockchain is illustrated in accordance with one or more embodiments described herein. Repetitive descriptions of similar elements used in other embodiments described herein are omitted for purposes of brevity.

Blockchain system 102 may add analysis blocks A and B (504 and 502, respectively) to data block 406. FIG. Analysis blocks 502 and 504 may be added as various processing steps and/or computational algorithms are performed on the data collected in data blocks 402 and 404 . In other embodiments, analysis blocks 502 and 504 may be formed after experiments have been performed on the logs of researchers, scientists, or other people involved in data analysis, or a combination thereof.

Logs are written, transcribed or otherwise converted into electronic form by researchers documenting the type of data analysis performed and the results of the analysis, and notes collected by the blockchain system. can contain. In other embodiments, logs may be obtained from the researcher's console or from other applications run during the analysis step.

One or more of analysis blocks 502 and 504 may correspond to different sets of analysis performed on the data. For example, if a first computation is performed and results are obtained, analysis block 504 may document and record the analysis. Analysis block 502 then documents the analysis and outcome of the second calculation, if the second calculation and the second result are obtained separately or based on the results of the first calculation. can be recorded. In other embodiments, one or more of analysis blocks 502 and 504 may document all or part of the analysis performed on data blocks 402 and 404, respectively. In some embodiments, the data or headers of parsing blocks 502 and 504 may be encrypted.

One or more of the parsing blocks 502 and 504 parse information related to the source of the log file or the identifier of the researcher or group of researchers, the timestamp, and the hash or link to the previous block. can have a header containing For example, analysis block 504 may contain a link to the previous data block and block 502 may contain a link to block 504 .

In some embodiments, the blockchain may be linear, in which case the parsing blocks are linear based on the order in which the data are first collected and then added to the database or ledger. will be added to the blockchain. In other embodiments, the blockchain may fork as processing is performed and documented onto the dataset. This is because they are collected before one or more of the data sets are collected. In such embodiments, analysis block 502 may link directly to data block 402 and analysis block 504 may link directly to data block 404, as an example.

Referring now to FIG. 6, another block diagram 600 of an exemplary non-limiting public research blockchain is illustrated in accordance with one or more embodiments described herein. Repetitive descriptions of similar elements used in other embodiments described herein are omitted for purposes of brevity.

Diagram 600 illustrates an exemplary blockchain created by a blockchain system that includes an exemplary set of blocks, which are data blocks 402 and 404; It includes analysis blocks 504 and 502 , inspection component 602 and correction block 604 .

Data blocks 402 and 404 may contain research/experimental data collected by one or more of experiments, instruments, interviews, and other collection methods. The data in data blocks 402 and 404 may correspond to data from separate experiments, different runs, different researchers or participants, or combinations thereof, and the like. Analysis blocks 502 and 504 may document the analysis performed on the data in data blocks 402 and 404, with information relating to script modifications performed on the data blocks over time. can include For example, if data processing/modeling of a script is performed on the data in either data block 402 or 404, the results of the script and information about the script may be stored, recorded, or otherwise stored in analysis block 504. can be documented in other ways. If the script is modified or otherwise changed in any way, or if the same script is run on different sets of data, the results and information about the script are analyzed by analysis block 502 . can be documented in

A test component 602 may include a summary of the analysis performed, the results obtained, and other conclusions obtained in previous blocks in the blockchain. In some embodiments, the inspection component may also include headers for data blocks and analysis blocks for linking backwards. In an additional embodiment, the inspection component may have a header containing a hash that is the hash of the last parsed block.

In another embodiment, a blockchain system may include one or more protocols. Thereby, the checking component 602 is not added or otherwise registered to the blockchain until the checking component has taken the outputs of the parsing blocks and their hashes. Including a hash not only guarantees the identity of the resulting block in the blockchain, but also ensures that the hash can be used to verify the authenticity and performance of the block. As an example, a test component may include a researcher's console output that may be linked back to analysis blocks 502 and 504 . The parse block hash can be compared to the console output hash to ensure that no other modifications or changes have been made to the results and logs.

Correction block 604 may include a weighting, or ranking of relative importance, of the results obtained between the analysis block and the test component. For example, if a relatively small number of analytical steps or modifications of a data processing algorithm are required to achieve a postulated result, the conclusion of an experiment or study will be more accurate than if many modifications were made. It may have a higher weighted importance. By determining which of the parse blocks resulted in a result, the blockchain system can determine how many modifications are made, with the fastest parse block resulting in a result. If an analysis block eventually produced a result, that result may be weighted more highly than if a later analysis block resulted in a result. The blockchain system may also determine the weighting based on the types of modifications reported in analysis blocks 502 and 504.

Referring now to FIG. 7, another block diagram 700 of an exemplary, non-limiting system of headers and data portions of a data blockchain in accordance with one or more embodiments described herein is illustrated. Repetitive descriptions of similar elements used in other embodiments described herein are omitted for purposes of brevity.

Diagram 700 shows the structure of data blocks 402 and 404 . It should be appreciated that the structure of blocks 402 and 404 applies to other blocks in the blockchain (eg, analysis blocks 502 and 504, verification component 602 and correction block 604).

In some embodiments, one or more of blocks 402 and 404 may include headers 702 and 716, respectively, as well as data portions 710 and 722, respectively. Headers 702 and 716 may contain information (identifiers 706 and 720) that identifies the researcher, research group, experiment or project. Data blocks 402 and 404 also have a timestamp 704 that identifies the time the data block was created on the server or the time the data set in data portions 710 and 716 was received or collected by the server. and 718.

Data block 402 also performs a hash of the data in data portion 710, or in some embodiments, is responsive to a hash function that performs a hash of some or all of the data in data block 402 including header 702. may include a hash A 714, which is a number of predefined length received as A hash is a unique number based on the content of the data, and even minor modifications to that data can result in distinctly different hash values.

Hash A 714 of data block 402 may be included in the header of subsequent block 404 . By linking hashes A 714 at 404, a blockchain is formed and any modification of the data can result in non-matching hashes, breaking the blockchain.

In various embodiments, headers 702 and 716 may also include URLs that link to data in one or more public or private databases/ledgers.

In some embodiments, headers 702 and 716 or data portions 710 and 722 may be encrypted to protect sensitive information. A researcher ID can be linked to a public key that can facilitate decryption of encrypted data and/or headers. In some embodiments, the data may be encrypted while the header is not encrypted along with the hash and identifier. This allows researchers to keep their data private while allowing reviewers to reproduce their results using encrypted data.

Referring now to FIG. 8, a flowchart of an exemplary, non-limiting, computer-implemented method for forming a blockchain based on research data and analytics according to one or more embodiments described herein. 800 is illustrated.

The method may begin at 802. At 802, the method includes receiving, by a device operatively coupled to the processor, a first file having experimental data (eg, by data collection component 106). The first file may be received from a public database or ledger or from a private database. This file may contain one or more datasets corresponding to each participant or experiment performed.

The method may continue at 804 . At 804, the method includes generating, by the device, a first block from the first file, where the first block includes a first header and experimental data, a header further includes a first timestamp, an identifier identifying the source of the experimental data, and a first hash based on the experimental data (eg, by data collection component 106). The first block may consist of many blocks corresponding to one or more of the separate data sets. One or more of the blocks may have respective headers each having a timestamp, an identifier and a hash. A hash of the data portion of the preceding block can be included in the header of subsequent blocks to provide a link in the blockchain and to provide reliability and tamper resistance to the blockchain. .

The method may continue at 806, where the method includes receiving a second file containing a log of the analysis performed on the experimental data by the device (eg, by analysis component 108). include. The analysis log may include not only the results of the data analysis, but also some or all of the data analysis and scripts associated with the processing.

The method continues at 808 with generating a second block based on the second file by the device. The second block includes a log of analysis, a second header that includes a second timestamp, a link to the first block, and a second hash based on the log of analysis (eg, by analysis component 108). including.

Referring now to FIG. 9, a flowchart of an exemplary, non-limiting, computer-implemented method for forming a blockchain based on research data and analytics according to one or more embodiments described herein. 900 is illustrated. Repetitive descriptions of similar elements used in other embodiments described herein are omitted for purposes of brevity.

The method may begin at 902, where the method includes forming, by a device operatively coupled to a processor, a blockchain representing a research project, where the blockchain includes a first block of study data and a second block of analysis data representing a log of analyzes performed on the study data (eg, by data collection component 106 and analysis component 108).

The method may continue at 904, where the method includes forming, by the device (eg, by test component 110), a summary block that includes a summary of study data and a summary of analysis data. .

The method may continue at 906, where the method includes adding a summary block to the blockchain by the device (eg, by inspection component 110).

For simplicity of explanation, the computer-implemented method is shown and described as a series of acts. It is to be understood and appreciated that the subject innovation is not limited to the illustrated acts or order of acts, or both; , and other operations may not be presented or described herein. Additionally, not all illustrated acts may be required to implement a computer-implemented methodology in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that a computer-implemented method could alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, the computer-implemented methods disclosed below and throughout this specification may be stored on an article of manufacture to facilitate transfer and transport of such computer-implemented methods to computers. It should be recognized that it is possible to Where the term article of manufacture is used herein, it is intended to include a computer program accessible from any computer-readable device or storage medium.

Further, because the composition of the data packets and/or the communication between the processor and the allocating component is established from a combination of electrical and mechanical components and circuits, a human can The subject communication between the processor and the allocation component, or both, cannot be reproduced or performed. For example, humans cannot generate data for transmission over wired and/or wireless networks between the processor and the allocation component, and so on. Furthermore, humans cannot packetize data, which can include a series of bits corresponding to information generated during a machine learning process (e.g., a blockchain formation process), nor can a machine learning process (e.g., a blockchain formation process) such as not being able to transmit data that may contain a series of bits corresponding to information generated during .

To provide context for various aspects of the disclosed subject matter, FIG. 10 and the following discussion are intended to provide a general description of suitable environments in which various aspects of the disclosed subject matter may be implemented. is intended to FIG. 10 illustrates a block diagram of an exemplary, non-limiting operating environment in which one or more embodiments described herein may facilitate. For purposes of brevity, repetitive descriptions of similar elements used in other embodiments described herein are omitted. Referring to FIG. 10, a suitable operating environment 1000 for implementing various aspects of the disclosure can also include a computer 1012 . Computer 1012 may also include processor 1014 , system memory 1016 , and system bus 1018 . A system bus 1018 couples system components including, but not limited to, system memory 1016 to processor 1014 . Processor 1014 can be any of a variety of available processors. Dual microprocessors and other multi-processor architectures may also be used for processor 1014 . System bus 1018 includes, but is not limited to, Industrial Standard Architecture (ISA), Micro Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus. (VLB), Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and Small Computer Systems Interface ( of several types, including a memory bus or memory controller, a peripheral or external bus, or a local bus, using any of a variety of available bus architectures, including SCSI), or combinations thereof. It can be any of the bus structures. System memory 1016 may also include volatile memory 1020 and nonvolatile memory 1022 . The basic input/output system (BIOS) contains the basic routines for transferring information between elements internal to computer 1012, such as during start-up. It is stored in non-volatile memory 1022 . By way of example, and not limitation, non-volatile memory 1022 may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory. memory, or non-volatile random access memory (RAM) (eg, ferroelectric RAM (FeRAM)). Volatile memory 1020 can also include random access memory (RAM), which can act as external cache memory. By way of illustration and not limitation, RAM may include static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synclink ( Synchlink DRAM (SLDRAM), Direct Rambus RAM (DRRAM), Direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM.

Computer 1012 may also include removable/non-removable, volatile/non-volatile computer storage media. FIG. 10, for example, illustrates disk storage 1024 . Disk storage 1024 may be, but is not limited to, a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memory stick. devices. Disk storage 1024 may also include storage media, separately or in combination with other storage media, including, but not limited to, compact disk ROM devices (CDs). -ROM), CD-recordable drives (CD-R drives), CD-rewritable drives (CD-RW drives) or optical disk drives such as Digital Versatile Disk ROM Drives (DVD-ROM). To facilitate connection of disk storage 1024 to system bus 1018, a removable or non-removable interface is typically employed such as interface 1026. FIG. FIG. 10 also illustrates software that acts as an intermediary between users and the basic computer resources described in suitable operating environment 1000 . Such software may also include operating system 1028, for example. An operating system 1028 , which may be stored in disk storage 1024 , functions to control and allocate the resources of computer 1012 . System applications 1030 employ, through program modules 1032 and program data 1034 , the management of resources stored, for example, in either system memory 1016 or disk storage 1024 by operating system 1028 . It should be appreciated that this disclosure may be implemented using various operating systems or using a combination of operating systems. An entity enters commands or information into computer 1012 through input device(s) 1036 . Input devices 1036 include, but are not limited to, pointing devices such as mice, trackballs, styluses, touch pads, keyboards, microphones, joysticks, game pads, satellite dishes, scanners, TV tuner cards. , digital cameras, digital video cameras, web cameras, etc. These and other input devices connect through system bus 1018 to processor 1014 via interface port 1038 . Interface ports 1038 include, for example, serial ports, parallel ports, game ports, and universal serial bus (USB). Output devices 1040 use some of the same types of ports as input devices 1036 . Thus, for example, a USB port can be used to provide input to computer 1012 and to output information from computer 1012 to output device 1040 . Output adapter 1042 is provided to illustrate that there are some output devices 1040 such as monitors, speakers, and printers, among other output devices 1040, that require special adapters. Output adapters 1042 include, by way of example and not limitation, video and audio cards that provide connectivity between output devices 1040 and system bus 1018 . It should be noted that other devices and/or systems of devices, such as remote computer 1044, provide both input and output functionality.

Computer 1012 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer 1044 . Remote computer 1044 can be a computer, server, router, network PC, workstation, microprocessor-based appliance, peer device, or other common network node, etc., but is also generally the same as computer 1012. may include many or all of the elements described in relation to For purposes of simplicity, only memory storage device 1046 is illustrated along with remote computer 1044 . Remote computer 1044 is logically connected to computer 1012 through network interface 1048 and physically connected via communications connection 1050 . Network interface 1048 spans wired and/or wireless communication networks such as local area networks (LAN), wide area networks (WAN), cellular networks, and the like. LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring, and others. WAN technologies include, but are not limited to, point-to-point links, circuit-switched networks such as Integrated Services Digital Networks (ISDN) and variations thereof, packet-switched networks, and Digital Subscriber Lines (DSL). include. Communications connection 1050 refers to hardware/software used to connect network interface 1048 to system bus 1018 . Communication connection 1050 is shown internal to computer 1012 for illustrative clarity, but could be external to computer 1012 . Hardware/software for connection to network interface 1048 are, by way of example only, regular telephone grade modems, modems including cable and DSL modems, ISDN adapters, and Ethernet cards. may include internal and external technologies of

Referring now to Figure 11, an illustrative cloud computing environment 1150 is shown. As shown, the cloud computing environment 1150 includes, for example, a personal digital assistant (PDA) or mobile phone 1654A, a desktop computer 1154B, a laptop computer 1154C, or an automotive computer system 1154N or any of these. Combinations, etc., include one or more cloud computing nodes 1110 with which local computing devices used by cloud consumers may communicate. Nodes 1110 may communicate with each other. They may be physically or virtually grouped in one or more networks, such as private clouds, community clouds, public clouds, or hybrid clouds, as described above, or combinations thereof (Fig. not shown). This allows cloud computing environment 1150 to offer infrastructure, platform and/or software as a service that does not require cloud consumers to keep resources on their local computing devices. Become. The types of computing devices 1154A-N shown in FIG. 11 are intended for illustration only, and which types of computing nodes 1110 and cloud computing environments 1150 are It is understood that any type of computerized device can communicate over a network and/or a network addressable connection (eg, using a web browser).

Referring now to Figure 12, a set of functional abstraction layers provided by cloud computing environment 1150 (Figure 11) is shown. It should be foreseen that the components, layers, and functions shown in FIG. 12 are intended to be illustrative only, and that embodiments of the present invention are not so limited. As shown, the following layers and corresponding functions are provided.

Hardware and software layer 1260 includes hardware and software components. Examples of hardware components include mainframes 1261 , reduced instruction set computer (RISC) architecture-based servers 1262 , servers 1263 , blade servers 1264 , storage devices 1265 , and network and networking components 1266 . In some embodiments, the software components include network application server software 1267 and database software 1268 .

Virtualization layer 1270 provides an abstraction layer. From this layer, the following examples of virtual entities may be provided: virtual servers 1271 , virtual storage 1272 , virtual networks 1273 including virtual private networks, virtual applications and operating systems 1274 , and virtual clients 1275 .

In one example, management layer 1280 may provide the functionality described below. Resource provisioning 1281 provides dynamic procurement of computing and other resources used to perform tasks within the cloud computing environment. Metering and Pricing 1282 provides cost tracking as resources are used within the cloud computing environment and billing or billing for consumption of those resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, in addition to protection for data and other resources. User portal 1283 provides access to the cloud computing environment for consumers and system administrators. Service level management 1284 provides allocation and management of cloud computing resources such that required service levels are met. Service level agreement (SLA) planning and fulfillment 1285 provides for the pre-positioning and procurement of cloud computing resources expected to be required in the future according to SLAs.

Workload tier 1290 provides examples of functions for which the cloud computing environment can be used. Non-limiting examples of workloads and functions that can be provided from this tier are mapping and navigation 1291, software development and lifecycle management 1292, virtual classroom teaching delivery 1293, data analysis processing 1294, transaction processing 1295, and transaction processing. Includes model software 1296 .

The present invention can be a system, method, apparatus or computer program product, or combination thereof, in any possible level of technical detail of integration. The computer program product may include computer readable storage medium(s) having thereon computer readable program instructions for causing a processor to perform aspects of the present invention. A computer-readable storage medium may be a tangible device capable of retaining and storing instructions for use by an instruction-executing device. A computer-readable storage medium can be, for example, without limitation, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or a suitable combination of any of the above. A non-exclusive list of more specific examples of computer readable storage media also includes portable computer diskettes, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable memory. Read Only Memory (EPROM or Flash Memory), Static Random Access Memory (SRAM), Portable Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD), It may also include mechanically encoded devices such as memory sticks, floppy disks (R), punch cards or raised structures in grooves in which instructions are recorded, and any suitable combination of the above. Computer readable storage media, as used herein, refers to radio waves or other freely propagating electromagnetic waves, propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables). It shall not be interpreted as an electromagnetic wave or as a transient signal per se, such as an electrical signal transmitted through a wire.

The computer-readable program instructions described herein can be transferred from a computer-readable storage medium to a respective computing/processing device or via, for example, the Internet, a local area network, a wide area network or a wireless network or the like. may be downloaded to an external computer or external storage device via a network such as a combination of A network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers or edge servers, or combinations thereof. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and stores the computer-readable program instructions on a computer-readable storage medium internal to the respective computing/processing device. to transfer. Computer readable program instructions for performing operations of the present invention include assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, configuration instructions for integrated circuits. data, or source code or object code, where source code and object code refer to object-oriented programming languages such as Smalltalk, C++, and the "C" programming language or similar. It may be described as any combination of one or more programming languages, including procedural programming languages, such as programming languages. Computer-readable program instructions may be used when executed wholly on a user's computer, when partly executed as a stand-alone software package on the user's computer, partly on the user's computer, and partly on a remote computer. , or may be run entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or ( Connections may be made to external computers (via the Internet, for example, using an Internet service provider). In some embodiments, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) are programmed with a computer readable program to implement aspects of the invention. The state information of the instructions may be used to execute the computer readable program instructions by customizing its electronic circuitry.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. . These computer readable program instructions are identified in one or more blocks of the flowcharts and/or block diagrams that are executed via a processor of a computer or other programmable data processing apparatus. A processor of a general purpose computer, special purpose computer or other programmable data analysis apparatus may constitute a machine so as to produce means for implementing the functions/acts. These computer-readable program instructions represent an article of manufacture in which the computer-readable storage medium on which the instructions are stored contains instructions that implement the functions/acts identified in one or more blocks of the flowcharts and/or block diagrams. configured to be stored on a computer-readable storage medium and capable of instructing a computer, programmable data processing apparatus or other device, or combination thereof, to function in a particular manner. good. Computer readable program instructions refer to the functions specified in one or more blocks of the flowcharts and/or block diagrams executed on a computer or other programmable apparatus or device. a computer or other programmable data processing apparatus or other device that is loaded onto a computer or other programmable apparatus or other device to cause a computer-implemented process to implement an action; device to perform a series of arithmetic operations.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions containing one or more executable instructions to implement the specified logical function. In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes appear in the reverse order, depending on the functionality involved. can also be executed with Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, represents the combination of specialized hardware and computer instructions that perform a particular function or operation or that perform a particular function or operation. It should also be noted that the combination may be implemented by a dedicated hardware-based system that performs the combination.

Although the present subject matter has been described above in the general context of computer-executable instructions for a computer program product running on one computer and/or on multiple computers, those skilled in the art will appreciate that the present subject matter It will be appreciated that the disclosure can be implemented or can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks and/or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the computer-implemented methods of the present invention can be applied to computers, handheld computing devices (e.g., PDAs, phones), microprocessor-based or programmable consumer or industrial electronics, and the like. Additionally, it will be appreciated that it may be practiced with other computer system configurations, including single or multiprocessor computer systems, mini-computing devices, and mainframe computers. The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. However, some, if not all aspects of the disclosure may be practiced on stand-alone computers. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

As used in this application, terms such as "component," "system," "platform," and "interface" relate to a computer-related entity or computing machine having one or more specific functions. May refer to and/or include entities. The entities disclosed herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, a thread of execution, a program, or a computer, or a combination thereof. By way of illustration, both the application running on the server and the server can be components. One or more components can reside in a process or thread of execution or both; a component can be localized on one computer; or both. In another example, respective components can execute from various computer readable media having various data structures stored thereon. Components may communicate via local and/or remote processes, such as by following a signal with one or more data packets (e.g., data from a component may communicate via a signal , interacting with other components in a local system, with another system in a distributed system, or with other systems across a network such as the Internet, or a combination thereof). In another example, a component can be a device that has a specific function provided by mechanical parts operated by an electrical or electronic circuit, but the electrical or electronic circuit is software or software executed by a processor. Operated by a firmware application. In such cases, the processor, which may be internal to the device or external to the device, may execute at least part of a software or firmware application. As yet another example, a component can be a device that provides a particular functionality through an electronic component without mechanical parts, where the electronic component is software that provides, at least in part, the functionality of the electronic component. or may include a processor or other means for executing firmware. In some aspects, components may emulate electronic components, for example, via virtual machines within the cloud computing system.

Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless stated otherwise, or clear from context, "X uses A or B" refers to either of the natural inclusive permutations. meant to mean That is, if X uses A, X uses B, or X uses both A and B, then "X uses A or B" means any of the above examples. is satisfied in Furthermore, the articles "a" and "an" as used in this specification and the accompanying drawings are directed to the singular unless otherwise indicated, or where the context clearly indicates that Otherwise, it should generally be construed to mean "one or more." As used herein, the terms "example" and/or "exemplary" are used to serve as examples, instances, or illustrations. For the avoidance of doubt, the subject matter disclosed herein is not limited to such examples. Furthermore, any aspect or design described herein as the term "example" and/or "exemplary" is necessarily construed as preferred or advantageous over other aspects or designs. It is not intended to exclude equivalent exemplary structures and techniques well known to those skilled in the art.

As used herein, the term "processor" includes, but is not limited to, single-core processors, single processors with software multi-threading capability, multi-core processors, software multi-core processors. May refer to virtually any computing processor or device, including multicore processors with threaded execution capability, multicore processors with hardware multithreading technology, parallel platforms, and parallel platforms with distributed shared memory . Further, processors include integrated circuits, application specific integrated circuits (ASICs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), programmable logic controllers (PLCs), complex programmable controllers (FPGAs), may refer to logic devices (CPLDs), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein could be. Processors may also utilize nanoscale architectures such as, but not limited to, molecular and quantum dot based transistors, switches and gates to optimize space usage or enhance performance of user equipment. can be. A processor may also be implemented as a combination of computing processors. In this disclosure, terms such as "store", "storage", "data store", "data storage", "database", and substantially any other information storage component related to the operation and functionality of the component The term is used to refer to a "memory component," an entity embodied in "memory," or a component that contains memory. Memory and/or memory components described herein may be either volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. It should be recognized that By way of illustration, and not limitation, non-volatile memory includes read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or may include non-volatile random access memory (RAM) (eg, ferroelectric RAM (FeRAM)). Volatile memory can include, for example, RAM, which can act as external cache memory. By way of example, and not limitation, RAM may include synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), sync It is available in many forms such as Link DRAM (SLDRAM), Direct Rambus RAM (DRRAM), Direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM). Moreover, the disclosed memory components of systems or computer-implemented methods herein are intended to include, but are not limited to, these and any other suitable types of memory.

What has been described above includes merely example systems and computer-implemented methods. Of course, it is impossible to describe every conceivable combination of components or computer-implemented methods for the purposes of describing this disclosure, but many further combinations and permutations of this disclosure are possible to those skilled in the art. can recognize that. Further, to the extent they are used in the detailed description, claims, appendices and drawings, terms such as "include," "have," and "possess" may be used to refer to certain claims. It is intended to be as inclusive as the term "comprising" is to be interpreted when the term "comprising" is used as a transitional term in a section. Descriptions of various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limiting to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein are defined as best describing principles of embodiments, practical applications, or technical improvements over technology found on the market, or otherwise disclosed herein by a person of ordinary skill in the art. It has been chosen so that the preferred embodiment can be understood.

Claims (23)

A computer-implemented method comprising:
generating, by a device operatively coupled to a processor, a first block from a first file, the first block including a first header and experimental data of the first file; included therein, wherein the first header further includes a first timestamp, an identifier identifying a source of the experimental data, and a first hash based on the experimental data; When,
generating a second block based on a second file containing a log of analysis performed on the experimental data by the device, wherein the second block includes the log of the analysis; , a second header including a second timestamp and a link to the first block and a second hash based on the log of the analysis;
A computer-implemented method, comprising: 2. The computer-implemented method of claim 1, further comprising forming, by the device, a blockchain based on the first block and the second block. 2. The computer-implemented method of claim 1, further comprising transmitting, by the device, the first block and the second block to a ledger database. generating, by the device, a third block containing a summary of the experimental data and the analysis and results of the analysis;
The device associates the third block with the first block and the second block by placing a link to the second block in a third header of the third block. adding to a blockchain that
The computer-implemented method of claim 1, further comprising: performing, by the device, corrections to the results of the analysis based on evaluation of the summary of the experimental data of the third block;
generating, by the device, a fourth block containing the correction;
adding the fourth block to the blockchain by the device;
5. The computer-implemented method of claim 4, further comprising: Further matching, by the device, hashes of each of the first block, the second block, the third block, and the fourth block to corresponding hashes of corresponding blocks in a ledger database. 6. The computer-implemented method of claim 5, comprising: 2. The computer implementation of claim 1, further comprising encrypting, by the device, the experimental data and the log of the analysis prior to forming the first block and the second block, respectively. Method. said second block containing information relating to a data processing script associated with said experimental data, said method comprising:
2. The computer-implemented method of claim 1, further comprising generating, by the device, a fifth block containing information relating to results of execution of the data processing script. The generating the first block comprises:
2. The computer-implemented method of claim 1, further comprising generating the first block based on a combination of multiple data blocks. a system,
a memory for storing computer-executable components;
a processor executing the computer-executable components stored in the memory;
wherein said computer-executable component comprises:
A data collection component that creates a master data block from a data entry blockchain, said data entry blockchain comprising a group of linked data entry blocks, said master - a data block includes a first header and data from said group of data entry blocks, said header including a first timestamp, an identifier identifying a source of said data; a first hash based on the data collection component;
a log of analysis performed on the data, and a second header including a second timestamp, a link to the master data block, and a second hash based on the log of the analysis. an analysis component that creates an analysis block containing
system, including The computer-executable component comprises:
11. The inspection component of claim 10, further comprising an inspection component that creates a summary block that includes the summary of the data, the summary of the analysis, and the results of the analysis, and a third header that includes a link to the analysis block. system. The computer-executable component comprises:
further including a correction component that assesses reliability of the results of the analysis based on the summary of the analysis and the log of the analysis, wherein the reliability is based on attempts to achieve the results of the analysis; 12. The system of claim 11, associated with number of times. 11. The system of claim 10, wherein the collection of data entry blocks are obtained from a public ledger database. 11. The system of claim 10, wherein the link to the master data block in the parse block is a first hash. 11. The system of claim 10, wherein said group of data entry blocks are associated with respective data collection sessions. A computer-implemented method comprising:
forming, by a device operatively coupled to a processor, a blockchain representing a research project, said blockchain comprising a first block of research data and analysis performed on said research data; a second block of analytical data representing a log;
forming, with the device, a summary block containing a summary of the study data and a summary of the analysis data;
adding the summary block to the blockchain by the device;
A computer-implemented method, comprising: further comprising assessing, by the device, reliability of study results associated with the analyzed data, wherein the reliability is based on the summary of the analyzed data and the log of the analysis; and 17. The computer-implemented method of claim 16, associated with a revision count. The forming of the blockchain comprises:
generating, by the device, the first block of research data including a first header and the research data, the header identifying a first timestamp and a source of the research data; said generating, further comprising an identifier and a first hash based on said research data;
generating, by the device, the second block of analysis data including the log of the analysis and a second header, the second header including a second timestamp and the study data. said generating including a link to a first block and a second hash based on said log of said analysis;
17. The computer-implemented method of claim 16, further comprising: 19. The computer-implemented method of Claim 18, wherein the link to the first block is the first hash. 17. The computer-implemented method of claim 16, further comprising obtaining, by the device, the first block of research data and the second block of analytical data from a public database. a system,
a memory for storing computer-executable components;
a processor executing the computer-executable components stored in the memory;
wherein said computer-executable component comprises:
A data collection component,
receiving a first file having experimental data;
generating a first block from the first file, the first block including a first header and the experimental data, the header including a first timestamp and a source of the experimental data; and a first hash based on the experimental data;
an analysis component that generates a second block based on a second file containing a log of an analysis performed on the experimental data, the second block including the log of the analysis and a second header; and wherein the second header includes a second timestamp, a link to the first block, and a second hash based on the log of the analysis;
an inspection component that generates a third block that includes a summary of the experimental data, the analysis, and results of the analysis;
system, including The inspection component also:
adding the third block to the blockchain associated with the first block and the second block by placing a link to the second block in a third header of the third block; , the computer-executable component comprising:
a correction component for assessing the reliability of the results of the analysis, wherein the reliability is based on the summary of the analysis data and the log of the analysis and is related to the number of modifications to the analysis procedure; 22. The system of claim 21, further comprising said correction component. A computer program product for causing a computer to perform the steps of the method according to any one of claims 1 to 9 or claims 16 to 20.

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